Air pollution and subclinical airway inflammation in the SALIA cohort study.

Vossoughi et al. Immunity & Ageing 2014, 1 1:5
httpy/www.immunityageing.com/content/l 1/1/5

IMMUNITY & AGEING

RESEARCH Open Access

Air pollution and subclinical airway inflammation
in the SALIA cohort study

Mohammad Vossoughi^'', Tamara Schikowski^'^'^, Andrea Vierk6tter\ Dorothea Sugiri\ Barbara Hoffmann^'^
Tom Teichert^, Christian Herder^'^ Thomas Schulte^, Christian Luckhaus^, Monika Raulf-Heimsoth^,
Swaantje Casjens^, Thomas Bruning^ and Ursula Kramer^

Abstract

Background: The association between long-term exposure to air pollution and local inflammation in the lung has
rarely been investigated in the general population of elderly subjects before. We investigated this association in a
population-based cohort of elderly women from Germany.

Methods: In a follow-up examination of the SALIA cohort study in 2008/2009, 402 women aged 68 to 79 years
from the Ruhr Area and Borken (Germany) were clinically examined. Inflammatory markers were determined in
exhaled breath condensate (EBC) and in induced sputum (IS). We used traffic indicators and measured air pollutants
at single monitoring stations in the study area to assess individual traffic exposure and long-term air pollution
background exposure. Additionally long-term residential exposure to air pollution was estimated using land-use
regression (LUR) models. We applied multiple logistic and linear regression analyses adjusted for age, indoor mould,
smoking, passive smoking and socio-economic status and additionally conducted sensitivity analyses.

Results: Inflammatory markers showed a high variability between the individuals and were higher with higher
exposure to air pollution. NO derivatives, leukotriene (LT) B4 and tumour necrosis factor-a (TNF-a) showed the
strongest associations. An increase of 9.42 |jg/m^ (interquartile range) in LUR modelled NO2 was associated with
measureable LTB4 level (level with values above the detection limit) in EBC (odds ratio: 1.38, 95% CI: 1.02 -1.86) as
well as with LTB4 in IS (%-change: 19%, 95% CI: 7% - 32%). The results remained consistent after exclusion of
subpopulations with risk factors for inflammation (smoking, respiratory diseases, mould infestation) and after
extension of models with additional adjustment for season of examination, mass of IS and urban/rural living as
sensitivity analyses.

Conclusions: In this analysis of the SALIA study we found that long-term exposure to air pollutants from traffic and
industrial sources was associated with an increase of several inflammatory markers in EBC and in IS. We conclude
that long-term exposure to air pollution might lead to changes in the inflammatory marker profile in the lower
airways in an elderly female population.

Keywords: Particle exposure. Epidemiology, Inflammatory markers. Induced sputum. Exhaled breath condensate

* Correspondence: Mohammad.Vossoughi(5)IUF-Duesseldorf.de
^lUF - Leibniz Research Institute for Environmental Medicine, Auf'm
Hennekamp 50, Dusseldorf 40225, Germany
Full list of author information is available at the end of the article

O© 2014 Vossoughi et a!.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
BIoIVIGCI CGntrsI creative Commons Attribution License (http://creativecommons.0rg/licenses/by/2.O), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public
Domain Dedication waiver (http://creativecommons.0rg/publicdomain/zero/l.O/) applies to the data made available in this
article, unless otherwise stated.

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Background

It has been hypothesised that air pollution induced sub-
clinical lung inflammation leads to a release of stress sig-
nals and of humoral mediators in the airways, which can
spill over into the circulation and then contribute to func-
tional impairment and ageing of other tissues and organs
[1]. The first step of this cascade however, the association
between long-term air pollution and subclinical inflam-
mation of the airways, has rarely been investigated in the
general elderly population so far. In a group of elderly sub-
jects in Steubenville, Ohio, it could be shown that short-
term exposure to PM2.5 may lead to airway inflammation
as measured by the fraction of exhaled nitric oxide [2].
There are a few studies on susceptible or highly exposed
cohorts. For example, in a group of waste handlers, daily
exposure to bioaerosols over a short period of time in-
creased the number of neutrophils and induced the secre-
tion of interleukin (IL)-8 in the lower airways [3]. In a
population of traffic policemen chronically exposed to
traffic-related air pollution a statistically increased neutro-
phil cell count could be shown compared to a control
group of healthy subjects without any exposure to traffic-
related pollutants [4]. In a further investigation, living
close to a major road was associated with neutrophilic
bronchitis, asthma and decreased lung function in patients
with airway diseases [5].

There is evidence that air pollution can induce a sub-
sequent systemic inflammatory response. Alveolar macro-
phages play an important role in the association between
the inflammatory process in the lung and the systemic in-
flammation because these cells are responsible for inges-
ting and clearing inhaled pollutants [1]. The interaction of
macrophages with particulate matter leads to the increase
of their phagocytic activity, oxidant production and release
of other inflammatory markers such as tumour necrosis
factor-a (TNF-a) [6,7]. However, other cells are also able
to produce these mediators [1]. Additionally, it has been
shown that a range of inhaled substances stimulate alveo-
lar macrophages to produce proinflammatory cytokines
such as IL-1, IL-6, and IL-8 and TNF-a [8-11]. Salvi et al.
[12] showed a significant increase in neutrophils obtained
from bronchial biopsies and also from peripheral blood
6 h after a short-term (1 h) exposure to diesel exhaust in a
group of healthy volunteers. We are faced with a temporal
association between air pollution, various markers of air-
way inflammation and subclinical inflammation. We aim
to investigate the effect of both particulate matter (PM)
and nitrogen dioxide (NO2) on a range of correlated
inflammatory mediators of airway. Thus, it is of parti-
cular interest to assess not only the cell count, but also
other inflammatory cytokines, which are induced by cell
stimulation.

The above mentioned studies on the association of air
pollution and subclinical airway inflammation focused

on cohorts of either highly exposed or already diseased
and presumably more susceptible subjects. However, the
association of long-term exposure to air pollution has
rarely been investigated in the general population of
elderly women so far. Results from SALIA (Study on the
influence of air pollution on lung function, inflammation
and ageing),a cohort study of elderly German women,
already demonstrated the link between long-term traffic-
related air pollution and cardiovascular mortality [13],
mild cognitive impairment [14], impaired glucose regula-
tion [15], incidence of type 2 diabetes [16], accelerated
skin ageing [17] and objectively measured chronic ob-
structive pulmonary disease (COPD) [18]. We hypothe-
sise that in the SALIA study an air poUution-induced
subclinical inflammation in the lung results in inflam-
matory mediators into the blood circulation, causing
various downstream comorbidities. The SALIA study
offers a good opportunity to investigate subclinical in-
flammation in the lung in a general population of elderly
women. We therefore investigated the first step of this
pathway namely the influence of long-term exposure to
particulate matter and NO2 from traffic and industry on
the level of inflammatory markers in exhaled breath con-
densate (EBC) and in induced sputum (IS) in a cross-
sectional analysis including 402 elderly women of the
population-based SALIA cohort in 2008/2009.

Methods

Study design and population

The SALIA study was initiated in the early 1980s by the
State Government of North-Rhine Westphalia to investi-
gate the health effects of air pollution exposure in women.
The study population consists of women from the indus-
trialized Ruhr Area in Germany (Dortmund, Duisburg,
Herne, Gelsenkirchen, Essen) and two non-industrialized
rural areas north of the Ruhr area (Borken, Diilmen). Men
were not recruited because of the high occupational ex-
posure of many men in this area, where coal mining and
steel industry constituted the predominant sources of in-
come in the time period before the baseline examination.
Between 1985 and 1994 baseline examinations were con-
ducted in 4874 women who were 55 years of age at time
of recruitment. In 2006, a follow-up examination was
conducted to assess the change in respiratory symptoms
after a strong decline in concentrations of ambient air
pollutants had taken place in the Ruhr Area. Women from
Diilmen and Herne did not participate in the follow-up
examination because of organisational restrictions in the
local health departments. From 2116 (53% of surviving
participants) women who responded to a self-adminis-
tered postal questionnaire, 1639 women agreed to parti-
cipate in further clinical examinations. 834 women from
this group underwent a clinical examination at local study
centres in 2008/2009. The present analysis is based on the

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first 402 women at the age of about 74 who were
subjected to an extended collection of blood samples and
analysis of inflammatory markers.

Ethical approval

The ethical committee of the Ruhr University Bochum
had favourably reviewed the SALIA Study in 2006
(Registration number: 2732). All women gave their
written informed consent before the investigation.

Air pollution assessment

We applied GIS (Geographic Information System) for the
assessment of exposure. Using address coordinates of the
participating women, exposure to fine particles, NO2 and
traffic was estimated by three different methods:

First, data from monitoring stations maintained by the
State Environment Agency covering the Ruhr area in an
8-km grid were used to reflect medium to large-scale
spatial variation in air quality. Accordingly we defined
long-term air pollution background exposure as five-
year means of the years 2003-2007 of PMio (PM with
diameter < 10 [xm) and NO2. For this purpose we used
the nearest measurement station to the women's home
address.

Second, we used land-use regression (LUR) models and
data from a measurement campaign (2008/2009) gained
in the framework of the EU-ESCAPE study (European
Study of Cohorts for Air Pollution Effects) for the assess-
ment of individual long-term exposure. Concentrations of
pollutants were measured at 40 sites for NO2 and 20 sites
for air-borne PM in the study area based on fourteen-day
samples for each season and site. The validated land-use
regression models were used to assign estimated NO2,
PMio, PM2.5 and filter absorbance of PM2.5 (soot) concen-
trations to each individuals residential address [19,20].

Third, traffic exposure was characterized by (1) the
distance of the home address to the next major road,
defined as > 10,000 cars per day, and (2) the daily traffic
volume within a 100 meters buffer around the home,
calculated as the sum of the products of the number of
vehicles from all roads with > 5,000 cars per day multi-
plied with the street section length in the 100 m buffer.
Figure 1 shows the monitoring stations maintained by
the State Environment Agency and the residential ad-
dresses of the participants and the corresponding expos-
ure to the LUR-modelled PMio and NO2.

Assessment of subclinical inflammation

All examinations were conducted according to stan-
dardized protocols. EBC was collected via "Eco-Screen"
(VIASYS; Hochberg, Germany) from the participants.
We analysed pH, the mediators leukotriene (LT) B4 and
nitrate/nitrite, which reflect inflammation of the airways,
and 8-isoprostane prostaglandin F2a (8-iso PGF2a), which

reflects oxidative stress. Women with acute infections of
the respiratory tract were excluded from the examination.
The measurement of pH was done via a pH-electrode.
NO derivatives were measured by a colorimetric assay kit
from Alexis (Cayman Chemicals; Griinberg, Germany)
determining the total nitrate/nitrite concentration with a
sensitivity of 5 [iM, LTB4 and 8-iso PGF2a were measured
by a competitive enzyme immune assay (Assay Design,
Ann Arbor, MI, USA).

After conducting the EBC procedure, participants in-
haled vaporized isomolar saline solution for 10 minutes
and were then asked to provoke coughing. IS was col-
lected and processed according to Raulf-Heimsoth et al.
[21] and then analysed for soluble inflammatory mediators
and differential cell counts. After centrifugation, the cell-
free supernatants were aliquoted, stored at -80°C until
further analysis of soluble markers. The cell pellets were
re-suspended and eosinophils, macrophages, neutrophils,
epithelial cells and the total number of cells as the sum of
these cells were determined. For differential cell counts
cytospins were prepared, stained and counted by three
independent observers. The same method as administered
for the EBC examination was done to measure nitrate/
nitrite and LTB4 in IS. IL-8, IL-lp and TNF-a were mea-
sured using ELISA technique described in detail in Raulf-
Heimsoth et al. [21]. The Bradford protein assay was used
to determine total protein content. In addition, matrix
metalloproteinase-9 (MMP-9) was measured using a mo-
noclonal "sandwich" enzyme immunoassay.

Covariate assessment

We obtained information about a priori known potentially
confounding factors from a standardized interview. We
included smoking status (recorded as current, former and
never smoking), current passive smoking at home, educa-
tional level defined as the maximum years of schooling of
the woman or her husband as indicator of socio-economic
status (low: less than 10 years, medium: 10 years, high:
more than 10 years), age and indoor mould infestation
which showed an association with outcomes or exposures.
Heating with fossil fuels was selected as potential con-
founder but not included in the final model because it was
neither associated with inflammatory outcomes nor with
air pollution.

Additionally we used information about the mass of IS,
participants moving history, urban/rural living, chronic
airway diseases (COPD, asthma, and bronchitis) and sea-
son of the clinical examination in order to model the asso-
ciations for subpopulations or with additional covariates
as sensitivity analyses.

Statistical methods

Continuous variables of inflammation showed a skewed dis-
tribution and a skewed residual distribution and therefore

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were log-transformed for analysis. If more than 50% of the
values were below the detection limit or equal to zero,
continuous variables of inflammation were transformed to
binary variables using the detection limit or 0 as cut-off.
These were LTB4 in EBC (83.3% below detection limit), and
macrophages (56% = 0) and eosinophils (77% = 0) in IS. We
hypothesise that exposure to air pollution leads to lower
pH-values but higher values of other inflammatory markers.
Since we expected pH to be inversely associated with air
pollution, pH was transformed to 9-pH in order to obtain
estimates in the same direction comparable with the esti-
mates of other markers of inflammation. "9" was used for
this transformation as the highest value for pH in our
dataset. We applied multiple linear or logistic regression
analysis to estimate the effect of air pollution and traffic
exposure on inflammatory markers. Air pollution concen-
trations were entered as continuous terms except distance
to a major road which was entered in categories (distance <
100 m, distance > 100 m). After back-transformation of lin-
ear regression coefficients, percentage changes of inflamma-
tion variables and the corresponding 95% confldence
intervals (95% CI) were presented for an interquartile range
(IQR) increase in continuous air pollutants and for the
binary variable "living close to major road".

We estimated the associations in crude models (only
adjusted for age) and in full models additionally adjusted
for smoking status, passive smoking, educational level,
exposure to indoor mould and age. The linearity of the
exposure-response relationship was investigated via nat-
ural spline models in the statistical software R version
2.13.1 (package "splines"). AH other statistical analyses
were done with SAS version 9.2 (SAS Institute, Gary,
NC).

Sensitivity analyses

We investigated whether any effects were due to associa-
tions solely observed in specific subgroups of the
women. Therefore we repeated the analysis in subgroups
and present the effect estimates along with the original
estimates in a series of figures. Additionally we included
other covariates and tested different definitions of
variables. These sensitivity analyses include:

(1) Exclusion of women with indoor mould infestation
since mould infestation was already shown in SALIA to
be a strong risk factor for the development of inflamma-
tory reactions [22]. (2) Exclusion of currently and
formerly smoking women. (3) Exclusion of women with
diagnosed asthma or COPD or bronchitis at foUow-up
of study. (4) Exclusion of women with any change of
their residential address since the baseline examination
in order to test the stability of results. (5) TNF-a was
additionally transformed into binary categories and
logistic regression was conducted because just slightly
more than 50% of the values (55.3%) were over the

detection limit. (6) In an extended model we additionally
adjusted for season of the clinical examination. (7) In a
separate extended model we used an indicator for urban
or rural residential area of women (Ruhr Area vs. Bor-
ken) and additionally adjusted the models for this binary
variable in order to investigate, whether the estimates
change when attaching more weight to the within-area
air pollution contrast and whether unmeasured charac-
teristics of rural and urban living confound our esti-
mates. (8) We repeated the analyses with the continuous
variable for proximity to the next major road instead of
its binary variable in order to avoid information loss due
to the dichotomisation. (9) Finally, we adjusted for mass
of IS.

Results

Characteristics of participants

The characteristics of the 402 participants are shown in
Table 1. The women had a mean age of about 74 (SD =
2.6) years and the majority of them were non-smokers.
Women living in urban areas (N = 212) were more fre-
quently smokers (4.7% vs. 1.1% for current smoking,
18.9% vs. 12.1% for former smoking) and more often
exposed to indoor mould than women in the rural area
(N = 190) (16% vs. 8.9%). Distributions of air pollution
variables are presented in Table 2. Median levels of aU
air pollution variables were significantly higher for the
urban areas compared to the rural area. The median of
daily traffic load from major roads within 100 meters
buffer is equal to zero because the majority of women
were living further away than 100 meters from the next
major road. Women lived on average 3.2 km (standard
deviation = 2.3 km) away from the next monitoring
station of State Environment Agency. The distributions
of markers of inflammation in EBC and IS are presented
in Table 3. The geometric means of the concentrations
of most inflammatory markers in women from urban
area were higher than those from women living in rural
areas. These differences are most pronounced for NO
derivatives, TNF-a, neutrophils and the total number of
ceUs in sputum.

Results of main analyses

Figure 2 shows the association between inflammatory
markers and individually estimated exposures for an
increase by one IQR of air pollutant exposure in the ad-
justed model. Long-term exposure to modelled NO2 was
significantly associated with LTB4 in EBC (odds ratio:
1.38, 95% CI: 1.02 - 1.86) as weU as in IS (%-change:
19%, 95% CI: 7% - 32%). Additionally, an increase of
9.42 (ig/m^ in modeUed NO2 (IQR) was associated with
a 15.9% (95% CI: 1.3% - 32.6%) increase in the total
number of cells in IS. Modelled PMio, PM2.5 and PM2.5
absorbance showed the same pattern of effects. For

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Table 1 Characteristics of the study population in rural and in urban areas in 2008/2009

Total area (N =

402) Rural area (N = 190)

Urban area (N = 212)

p-Value^

Age (means (sd))

74.1 (2.6)

74.0 (2.5)

74.2 (2.6)

0.3259

Years of schooling (%)

0.8655

<10

17.0

18.1

16.1

=10

51.0

50.5

51.2

>10

32.0

31.4

32.7

Smoking (%)

0.0125

Current

3.0

1.1

4.7

Former

15.7

12.1

18.9

Never

81.3

86.8

76.4

Current passive smol<ing (%)

38.6

36.8

40.3

0.4797

Exposure to indoor mould (%)

12.7

8.9

16.0

0.0330

Asthma^ (%)

9.7

8.5

10.9

0.4214

COPD^ (%)

3.2

3.1

3.3

0.9351

Chronic bronchitis^ (%)

11.0

11.6

10.5

0.7109

Change of home address (%)

14.2

11.1

17.0

0.0889

^p-Value of f-test or Wilcoxon test for the difference of means between rural and urban area.
"^Reported as doctor-diagnosed diseases.

example a 2 (ig/m^ (IQR) increase in PM2.5 was asso- was increased by one IQR (990 vehicles*km/d), then the
ciated with a 16.2% (95% CI: 1.1% - 33.5%) increase in chance to have measurable LTB4 was 11% higher (95% CI:
NO derivatives in IS. Modelled PM2.5 was also signifi- 1.1% - 21.5%). In contrast proximity to a major road did
cantly associated with TNF-a (%-change: 15.8%, 95% CI: not show any consistent pattern of effects with markers of
2.6% - 30.8%). inflammation (Additional file 1: Figure SI).

Individually estimated traffic volume showed positive Long-term exposure as measured at single monitoring
associations with inflammatory markers. If traffic volume stations was also positively associated with the inflammatory

Table 2 Distribution of PM, NO

2 and traffic exposure in rural and in urban areas

Total area (N = 402) Rural area (N = 1 90)

Urban area (N = 212)

p-Value^

Variable

Median (IQR) Median (IQR)

Median (IQR)

Nearest monitoring stations (Five-year mean)^

NO2 [Mg/m']

30.8 (13.2) 20.2 (0)

33.4 (2.6)

< 0.0001

PM10 [Mg/m']

25.3 (3) 25.2 (0)

28.2 (3.4)

< 0.0001

LUR-modelled exposure''

NO2 [Mg/m']

26.0 (9.42) 23.0 (2)

31.9 (10.3)

< 0.0001

PM10 [Mg/m']

26.4 (2.26) 25.6 (0.89)

27.7 (2.7)

< 0.0001

PM2.5 [Mg/m']

1 7.4 (2.06) 1 7.0 (0.42)

18.9 (1.7)

< 0.0001

PM2.5 absorbance [10"^ m"^]

1.38 (0.44) 1.20 (0.13)

1 .62 (0.47)

< 0.0001

Traffic load^ [vehicle*km/day]

0 (990) 0 (0)
% %

0 (1330)
%

0.0003

Distance < 100 m"^

19.1 14.7

23.1

0.0331 ^

^Five-year mean of 2003 - 2007 from the nearest monitoring station of the State Environment Agency covering the area in an 8-km grid (1 station in Borken,
5 stations in Ruhr-Area).

"^Land-use regression modelled exposure using data from a measurement campaign (2008/2009) gained in the framework of the EU-ESCAPE study for assessment
of individual long-term exposure (Modelling based on the measurements from 40 stations for NO2 and 20 stations for PM).

^Traffic volume within a 100 m buffer around the home, calculated as the sum of the products of the number of vehicles from all roads with > 5,000 vehicles per
day multiplied with the street section length in the 100 m buffer.

'^Distance of residential address < 100 m from major road with more than 10,000 vehicles per day.

^p-Value of Wilcoxon test for the difference of means between rural and urban area.
VValue of x^-test for the difference between rural and urban area.

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Table 3 Distribution of inflammatory markers of study population in rural and in urban areas in 2008/2009

Total area

Rural area

Urban area

p-Value''

Geometric mean (gsd)^

N

Geometric mean (gsd)^

N

Geometric mean (gsd)^

N

Markers in exhaled breath condensate

pH

7.1 (1.1)

381

7.1 (1.1)

185

7.1 (1.1)

196

0.854

NO derivatives [pM]

6.3 (1.8)

384

6.0 (1.8)

186

6.6 (1.9)

198

0.1379

LTB4 [pg/ml]^

8.9 (1.4)

372

8.7 (1.4)

181

9.0 (1.4)

191

0.2522

8-iso-PGF 2 a [pg/ml]

111 (1.9)

363

112 (1.9)

175

110 (2.0)

188

0.8583

Soluble markers in induced sputum

Total protein content [|jg/ml]

207 (2.1)

324

196 (2.1)

160

219 (2.0)

164

0.1687

IL-8 [pg/ml]'

974 (3.1)

318

991 (3.2)

158

959 (3.0)

160

0.7991

NO derivatives [pM]

14.0 (2.2)

324

12.5 (1.9)

160

15.8 (2.4)

164

0.0072

IL-ip [pg/ml]

15.1 (2.9)

320

15.8 (3.1)

159

14.5 (2.7)

161

0.5007

TNF-a [pg/ml]

1 .8 (2.0)

320

1 .6 (2.0)

158

2.0 (2.0)

162

0.0067

LTB4 [pg/ml]

642 (2.2)

324

608 (2.1)

160

679 (2.2)

164

0.1988

MMP-9 [ng/ml]^

94.0 (4.5)

279

84.0 (4.9)

135

104 (4.2)

144

0.2431

Cells in induced sputum

Total number of cells (xlO^)

14.5 (2.7)

324

13.0 (2.7)

160

16.2 (2.6)

164

0.0433

Macrophages^

0.1 (19.2)

321

0.1 (18.3)

158

0.1 (20.3)

163

0.4582

Neutrophils (xloY

15.6 (6.2)

321

12.2 (6.4)

158

19.8 (5.9)

163

0.0175

Eosinophils^

0.03 (9.1)

321

0.03 (8.0)

158

0.04(10.1)

163

0.3601

^Geometric standard deviation, "^p-Value of f-test for the difference of geometric means between rural and urban area, ^Values > 20000 set to 20000, ^Values <2
set to 2, ^Transformed to binary variables because for LTB4 in EBC 83.3% of the values were below the detection limit and for macrophages 56% and for
eosinophils 77% of the values were equal to zero. Values > 5x10^ set to 5x10^; Values < 1 set to 1 for the log-transformation.

markers. Five-year mean of NO2 was significantly asso-
ciated with LTB4 and NO derivatives in EBC and with NO
derivatives and TNF-a in IS. Five-year mean of PMio
showed also significant association with LTB4 in EBC
(Additional file 1: Figure SI). Air pollution variables
showed mostly no associations with concentrations of
8-isoprostane and pH in EBC, and IL-8 and IL-1|3 in IS.

We conducted a stratified analysis for co-variables and
for urban/rural living. We did not find any effect modifica-
tion. Especially there was no indication that the effect was
stronger in one area compared to the other one (data not
shown). The crude associations differed only marginally
from the fully adjusted associations (Additional file 1:
Figure S2). We did not detect any significant deviations
from linearity for the exposure-response associations (data
not shown).

Results of sensitivity analyses

Exclusion of subpopulations or extension of models with
additional adjustment for covariables as sensitivity analyses
did not change the results relevantly (Additional file 1:
Figure S3-S8). Adjustment for urban/rural living atte-
nuated the associations for NO derivatives and TNF-a in
sputum and LTB4 in EBC. Estimates were attenuated for

modelled NO2 and modelled PM2.5 after adjustment for
urban/rural living. (Additional file 1: Figure S9). Effects for
TNF-a were in the same direction after transforming the
continuous variable into a binary one (Additional file 1:
Figure SIO). Finally, the associations with distance to
major road - modelled as a continuous variable - point
into the same direction as the results for the binary
variable (Additional file 1: Figure Sll).

Discussion

In this analysis within the SALIA study we found that
long-term exposure to air pollutants from traffic and in-
dustrial sources was associated with an increase of several
inflammatory markers in EBC and in IS. Inflammatory
markers showed a high interindividual variability. Amongst
others the already known associations between these
markers and smoking and mould infestation contributed
to this variability [22]. These markers were increased when
air pollution was higher. Significant associations of long-
term air pollution were shown for LTB4 and NO deriva-
tives in EBC and for total number of cells, NO derivatives,
TNF-a and LTB4 in IS. The most consistent associations
were found for LTB4 in EBC and changed between 11%
(95% CI: 1.1% - 21.5%) for traffic volume up to 104% (95%

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Modelled NOo

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Figure 2 Association of marlcers of inflammation with particles and NO2. Percentage changes and odds ratios with the corresponding 95%
confidence intervals for inflammatory markers in exhaled breath condensate (E)* and in induced sputum (S)* for an increase by one interquartile
range (IQR) of land-use regression modelled NO2 (A), PM10 (B), PM2.5 (C), PM2.5 absorbance (D), adjusted for age, smoking (current smoking,
former smoking, never smoking), current passive smoking, indoor mould and socio-economic status by years of schooling. Number of women for
each model: NO derivatives (E) =380, NO derivatives (S) =320, LTB4 (S) =320, pH (E) =377, 8-isoPGF2a (E) =360, IL-8 (S) =314, 11-1(3 (S) =316, TNF-a
(S) =316, Total protein (S) =320, MMP-9 (S) =275, number of cells (S) =321, neutrophils (S) =317, LTB4 (E) =369, macrophages (S) =317, eosinophils
(S) =317. ""Due to place restriction in the figures we did not use the same abbreviations for exhaled breath condensate (EBC) and induced sputum (IS)
as stated in the text.

CI: 27.7% - 226%) for five-year mean of NO2, respectively.
LTB4 is a potent chemoattractant of neutrophils and was
shown to contribute significantly to neutrophil influx into
the airway in COPD patients [23]. IL-8, IL-1|3, pH and
8-isoprostane were not associated with air pollution.

Few studies before have investigated long-term expo-
sure to air pollution and markers of local pulmonary in-
flammation. In patients with COPD, neutrophils, LTB4,
IL-8, macrophages, MMP-9 and TNF-a in airways were
shown to be increased in several studies [24-28], how-
ever little is known about their associations with long-
term air pollution in elderly women from the general
population. We found a statistically significant asso-
ciation of PM2.5 with NO derivatives in IS and of the
five-year mean NO2 exposure with NO derivatives in
EBC. This observation of an up-regulation of inflamma-
tory activity by long-term exposure to air pollution is
supported by a study on elderly subjects in Steubenville,

Ohio, which demonstrated an association between long-
term exposure to PM2.5 and the fraction of exhaled
nitric oxide (NO) [2].

Modelled long-term NO2 exposure was associated with
total cell count in IS, however none of the exposures were
clearly associated with neutrophil counts in sputum. This
is in contrast to a case-control study on policemen [4],
which found a statistically significant increase in the per-
centage of neutrophils in traffic policemen (median = 65,
IQR = 13.5) compared to healthy subjects (median = 40.5,
IQR = 9.5; p < 0.01) after a long-term exposure to traffic
pollutants. Similarly to our study, they did not find any
association of air pollutants with eosinophils, although
our analysis is based on a binary variable for eosinophils
indicating values below or above the detection limit.

Adjustment for urban/rural living attenuated the asso-
ciations particularly for NO derivatives and TNF-a in spu-
tum and LTB4 in EBC. This might be due to the lack of

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Page 9 of 10

exposure contrasts within the respective areas of resi-
dence. We did not include urban/rural living into the main
model in order to attach more weight to the between-area
air pollution contrast. It is therefore possible, that other,
unmeasured characteristics of rural and urban living could
have confounded our estimates. Extensive sensitivity ana-
lyses with additional covariates however did not yield
qualitatively different results.

Schikowski et al. [18] showed that long-term exposure
to high concentrations of air pollutants at baseline of the
SALIA study were associated with reduced lung function
and prevalence of COPD. Therefore it was not clear
whether an association of air pollution with local pulmon-
ary inflammatory markers demonstrated a direct effect of
current air pollution at follow-up of the SALIA study
(2008) or an indirect effect of air pollution at baseline
(1985-1994) mediated by a long-lasting effect through
established lung diseases with probable inflammation.
Therefore we excluded women with diagnosed bronchitis
or asthma or COPD. This exclusion did not change our
results. Therefore we consider our results as indication of
an effect of current air pollution on subclinical pulmonary
inflammation.

The strength of the study is that SALIA is a population-
based cohort of elderly women (not a cohort of either
highly exposed or diseased and more susceptible subjects)
with a detailed exposure assessment and an in-depth
characterization of subclinical pulmonary inflammation.
We assessed not only the LUR-modelled air pollution but
also air pollution at single monitoring stations in order to
consider the long-term spatial background air pollution in
addition to the individually modelled exposure. Different
findings for LUR-modelled and corresponding five-year
means of pollutants, which are most pronounced for LTB4
in IS, might be due to the better spatial resolution of the
LUR-modelled exposures. Traffic related air pollution is
better described with the LUR-models. However, the
effects for five-year means of exposures are in the same
direction as the effects for LUR-modelled exposure. One
limitation of the current study is that our study was based
on a subset of the cohort population which attended the
follow-up examination in 2008/2009 at a mean age of
74 years. It is therefore possible that the results are
attenuated by a healthy survivor effect.

Conclusions

In this population-based study we could show a cross-
sectional association between long-term exposure to air
poUutants and concentration of several inflammatory
markers in fluids collected from the lower respiratory
tract in an elderly female population. We conclude that
long-term exposure to air pollution might lead to
changes in the inflammatory marker profile in the lower
airways.

Additional file

Additional file 1: Figure 51. Association of markers of inflammation
with particles, NO2 and traffic exposure. Figure S2. Association of
markers of inflammation with particles and NO2 (crude model vs.
adjusted model). Figure S3. Association of markers of inflammation with
particles and NO2 (subpopulation without indoor mould vs. total
population). Figure S4. Association of markers of inflammation with
particles and NO2 (subpopulation without current and former smoking vs.
total population). Figure S5. Association of markers of inflammation with
particles and NO2 (subpopulation without COPD, asthma and bronchitis
vs. total population). Figure S6. Association of markers of inflammation
with particles and NO2 (subpopulation without change of residential
addresses vs. total population). Figure S7. Association of markers of
inflammation with particles and NO2 (model additionally adjusted for
season of examinations vs. model not adjusted for season). Figure S8.
Association of markers of inflammation with particles and NO2 (model
additionally adjusted for mass of induced sputum vs. model not adjusted
for mass of induced sputum). Figure S9. Association of markers of
inflammation with particles and NO2 (model additionally adjusted for
urban/rural living vs. model not adjusted for urban/rural living).
Figure S10. Association of a) continuous variable for TNF-a and b)
binary variable for TNF-a with particles, NO2 and traffic exposure.
Figure S11. Association of markers of inflammation with distance to
major road (continuous variable for distance vs. binary variable for
distance).

Abbreviations

COPD: Chronic obstructive pulmonary disease; EBC: Exhaled breath
condensate ("E" is used in the figures due to place restriction);
ESCAPE: European study of cohorts for air pollution effects; GIS: Geographic
information system; IL-1[3: lnterleukin-1 [3; IL-8: lnterleukin-8; IQR: Interquartile
range; IS: Induced sputum ("S" is used in the figures due to place restriction);
LTB4: leukotriene (LT) B4; LUR: Land-use regression; hAhAP-9: Matrix
metalloproteinase-9; NO2: nitrogen dioxide; OR: Odds ratio; PM: Particulate
matter; PMiq: Particulate matter with diameter < 10 |jm; PM2.5: Particulate
matter with diameter < 2.5 |jm; SALIA: Study on the influence of air pollution
on lung function inflammation and ageing; TNF-a: Tumour necrosis factor-a;
8-iso PGF2a: 8-isoprostane prostaglandin F2a; 95% CI: 95% confidence interval.

Competing interests

The authors report no competing interest.

Authors' contributions

Study idea and design: UK, TS, AV, MRH. Statistical analysis: MV. Interpretation
of results: MV, TS, AV, BH, TT, CH, ThS, CL, MRH, SC, TB, UK. Exposure
modelling: DS. Assessment of subclinical inflammation: MRH, SC. All authors
participated in manuscript preparation. All authors read and approved the
final manuscript.

Acknowledgments

We would like to thank German Research Foundation (DFG) for supporting this
investigation (DFG; HE-4510/2-1, KR 1938/3-1, LU 691/4-1). LUR modelled
exposures were provided in the frame of the European ESCAPE study from the
European Community's Seventh Framework Program (FP7/2007-201 1) under
grant agreement number: 21 1250. Road maps with emissions from traffic
and PM values from monitoring stations were maintained from the State
Environmental Agency of North-Rhine Westphalia (LANUV). The German
Diabetes Centre is funded by the German Federal Ministry of Health and the
Ministry of Innovation, Science and Research of the State of North-Rhine
Westphalia. This study was supported in part by a grant from the German
Federal Ministry of Education and Research (BMBF) to the German Center for
Diabetes Research (DZD e.V.). We would like to thank Dr Thomas Kuhlbusch
and Dr Ulrich Quass from Institute for Energy and Environmental Technology
(lUTA) for the exposure measurement We also thank all study participants for
the long lasting and continuous participation in the study.

Author details

^lUF - Leibniz Research Institute for Environmental Medicine, Aufm
Hennekamp 50, Dusseldorf 40225, Germany. ^Swiss Tropical and Public

Vossoughi et al. Immunity & Ageing 2014, 1 1:5
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Page 10 of 10

Health Institute, Socinstr. 57, Basel 4002, Switzerland. ^University of Basel,
Petersplatz 1, Basel 4003, Switzerland. ^Institute for Clinical Diabetology,
German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich
Heine University Dusseldorf, Aufm Hennekamp 65, Dusseldorf 40225,
Germany. ^German Center for Diabetes Research (DZD), partner site
Dusseldorf, Aufm Hennekamp 65, Dusseldorf 40225, Germany. ^Department
of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-University,
Bergische Landstr. 2, Dusseldorf 40629, Germany. ^Institute for Prevention
and Occupational Medicine of the German Social Accident Insurance,
Institute of the Ruhr-Universitat Bochum (IPA), Burkle-de-la-Camp-Platz 1,
Bochum 44789, Germany. ^Heinrich Heine University of Dusseldorf, Medical
Faculty, Moorenstrasse 5, Dusseldorf 40225, Germany.

Received: 27 September 2013 Accepted: 14 March 2014
Published: 19 March 2014

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doi:1 0.1 186/1 742-4933-1 1-5

Cite this article as: Vossoughi et al.: Air pollution and subclinical airway
inflammation in the SALIA cohort study. Immunity & Ageing 2014 1 1:5.

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